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Exploring Insulin-Related Gene Expression Across Tissues Using Weighted Models

This presentation discusses the detection of islet transcripts correlating with a weighted insulin model across various tissues. Utilizing multiQTL models and topological overlap matrices (TOM), the study uncovers genetic dependencies at key chromosome loci (Chr 2, 9, 12, 14, 16, 17, 19). It emphasizes the advantages of Horvath's modules for organizing expression traits and their clinical relevance, significantly reducing multiple comparison issues. Employing Ping Wang's modules, the research aims to enhance our understanding of the genetic architecture underlying insulin traits.

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Exploring Insulin-Related Gene Expression Across Tissues Using Weighted Models

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  1. Expression Modules Brian S. Yandell (with slides from Steve Horvath, UCLA, and Mark Keller, UW-Madison)

  2. Detected by scanone # transcripts that match weighted insulin model in each of 4 tissues: Weighted models for insulin Detected by Ping’s multiQTL model

  3. Ping Wang insulin main effects Chr 2 Chr 9 Chr 12 Chr 14 Chr 16 Chr 17 Chr 19 How many islet transcripts show this same genetic dependence at these loci?

  4. Expression NetworksZhang & Horvath (2005)www.genetics.ucla/edu/labs/horvath/CoexpressionNetwork • organize expression traits using correlation • adjacency • connectivity • topological overlap

  5. Using the topological overlap matrix (TOM) to cluster genes • modules correspond to branches of the dendrogram TOM plot Genes correspond to rows and columns TOM matrix Hierarchical clustering dendrogram Module: Correspond to branches

  6. module traits highly correlated • adjacency attenuates correlation • can separate positive, negative correlation • summarize module • eigengene • weighted average of traits • relate module • to clinical traits • map eigengene www.genetics.ucla/edu/labs/horvath/CoexpressionNetwork

  7. advantages of Horvath modules • emphasize modules (pathways) instead of individual genes • Greatly alleviates the problem of multiple comparisons • ~20 module comparisons versus 1000s of gene comparisons • intramodular connectivity ki finds key drivers (hub genes) • quantifies module membership (centrality) • highly connected genes have an increased chance of validation • module definition is based on gene expression data • no prior pathway information is used for module definition • two modules (eigengenes) can be highly correlated • unified approach for relating variables • compare data sets on same mathematical footing • scale-free: zoom in and see similar structure

  8. Ping Wang modules for 1984 transcripts with similar genetic architecture as insulin contains the insulin trait

  9. 17 Islet – modules 2 16 14 19 12 9 chromosomes Insulin trait

  10. Islet – enrichment for modules Insulin chromosomes

  11. www.geneontology.org • ontologies • Cellular component (GOCC) • Biological process (GOBP) • Molecular function (GOMF) • hierarchy of classification • general to specific • based on extensive literature search, predictions • prone to errors, historical inaccuracies

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